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EM: LUCI (Land Utilisation and Capability Indicator), New Zealand (EM-659)
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EM Identity and Description
EM Identification
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EM ID
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EM-659 |
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EM Short Name
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LUCI, New Zealand |
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EM Full Name
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LUCI (Land Utilisation and Capability Indicator), New Zealand |
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EM Source or Collection
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None |
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EM Source Document ID
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380 ?Comment:Document 381 is an additional source for this EM. |
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Document Author
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Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell |
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Document Year
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2017 |
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Document Title
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Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
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Contact Name
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Martha I. Trodahl |
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Contact Address
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School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand |
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Contact Email
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Not reported |
EM Description
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Summary Description
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ABSTRACT: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 |
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Specific Policy or Decision Context Cited
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Land management trade off between agricultural productivity and water quality |
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Biophysical Context
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Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. |
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EM Scenario Drivers
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No scenarios presented |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Method + Application |
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New or Pre-existing EM?
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Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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Doc-379 | Doc-381 |
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EM ID for related EM
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EM-658 |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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1930-2013 |
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EM Time Dependence
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time-stationary |
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EM Time Reference (Future/Past)
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Not applicable |
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EM Time Continuity
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Not applicable |
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EM Temporal Grain Size Value
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Not applicable |
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EM Temporal Grain Size Unit
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Not applicable |
EM Spatial Extent
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Bounding Type
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Watershed/Catchment/HUC |
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Spatial Extent Name
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Lake Rotorua catchment |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature |
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Spatial Grain Size
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5m x 5m |
EM Structure and Computation Approach
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EM Computational Approach
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Analytic |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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No |
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Model Goodness of Fit Reported?
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No |
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Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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No |
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Model Uncertainty Analysis Reported?
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No |
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Model Sensitivity Analysis Reported?
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No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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-38.14 |
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Centroid Longitude
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176.25 |
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Centroid Datum
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WGS84 |
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Centroid Coordinates Status
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Estimated |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland |
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Specific Environment Type
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Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Not applicable |
Taxonomic level and name of organisms or groups identified
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| None Available |
EnviroAtlas URL
em.detail.enviroAtlasURLHelp
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| The National Hydrography Dataset (NHD), Average Annual Precipitation, Total Annual Nitrogen Deposition |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM Variable Names (and Units)
Predictor
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Driving Variables (and Units)
view details (9 variables)
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Constant or Factor Variables (and Units)
view details (6 variables)
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Intermediate
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Intermediate (Computed) Variables (and Units)
view details (2 variables)
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Response
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